Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 1,596

The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.

RMSEA, CFI, and TLI in structural equation modeling with ordered categorical data: The story they tell depends on the estimation methods

Open access
Xia, Yan, Yang, Yanyun·2019·Behavior Research Methods

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.3758/s13428-018-1055-2

Cites

9 of 9 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 9 references

Research works printed but not linked: 39

  1. Barrett, P. (2007). Structural equation modelling: Adjudging model fit. Personality and Individual Differences, 42, 815–824. · DOI
  2. Beauducel, A., & Herzberg, P. Y. (2006). On the performance of maximum likelihood versus means and variance adjusted weighted least squares estimation in CFA. Structural Equation Modeling, 13, 186–203. · DOI
  3. De Beer, L. T., Pienaar, J., & Rothmann, S. (2014). Job burnout’s relationship with sleep difficulties in the presence of control variables: A self-report study. South African Journal of Psychology, 44, 454–466. · DOI
  4. Bentler, P. M. (2008). EQS structural equation modeling software. Encino, CA: Multivariate Software.
  5. Bentler, P. M., & Bonett, D. G. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological Bulletin, 88, 588–606. https://doi.org/10.1037/0033-2909.88.3.588 · DOI · Paper page
  6. Brosseau-Liard, P. E., & Savalei, V. (2014). Adjusting incremental fit indexes for nonnormality. Multivariate Behavioral Research, 49, 460–470. · DOI
  7. Brosseau-Liard, P. E., Savalei, V., & Li, L. (2012). An investigation of the sample performance of two nonnormality corrections for RMSEA. Multivariate Behavioral Research, 47, 904–930. · DOI
  8. Cudeck, R., & Browne, M. W. (1992). Constructing a covariance matrix that yields a specified minimizer and a specified minimum discrepancy function value. Psychometrika, 57, 357–369. · DOI
  9. Currier, J. M., & Holland, J. M. (2014). Involvement in abusive violence among Vietnam veterans: Direct and indirect associations with substance use problems and suicidality. Psychological Trauma: Theory, Research, Practice, and Policy, 6, 73–82. · DOI
  10. DiStefano, C., & Morgan, G. B. (2014). A comparison of diagonal weighted least squares robust estimation techniques for ordinal data. Structural Equation Modeling, 21, 425–438. · DOI · Paper page
  11. Fernandez, S., & Moldogaziev, T. (2013). Employee empowerment, employee attitudes, and performance: Testing a causal model. Public Administration Review, 73, 490–506. · DOI
  12. Garrido, L. E., Abad, F. J., & Ponsoda, V. (2016). Are fit indexes really fit to estimate the number of factors with categorical variables? Some cautionary findings via Monte Carlo simulation. Psychological Methods, 21, 93–111. · DOI
  13. Iglesias, K., Burnand, B., & Peytremann-Bridevaux, I. (2014). PACIC Instrument: Disentangling dimensions using published validation models. International Journal for Quality in Health Care, 26, 250–260. · DOI
  14. Johnson, D. R., & Creech, J. C. (1983). Ordinal measures in multiple indicator models: A simulation study of categorization error. American Sociological Review, 48, 398–407. · DOI · Paper page
  15. Jöreskog, K. G., & Sörbom, D. (1993). LISREL 8: Structural equation modeling with the SIMPLIS command language. Chicago, IL: Scientific Software International.
  16. Koziol, N. A. (2010). Evaluating measurement invariance with censored ordinal data: A Monte Carlo comparison of alternative model estimators and scales of measurement. Unpublished master’s thesis, University of Nebraska, Lincoln, NE.
  17. Lai, K., & Green, S. B. (2016). The problem with having two watches: Assessment of fit when RMSEA and CFI disagree. Multivariate Behavioral Research, 51 220–239. · DOI · Paper page
  18. Li, C. H. (2014). The performance of MLR, USLMV, and WLSMV estimation in structural regression models with ordinal variables (Doctoral dissertation). Michigan State University, East Lansing, MI.
  19. MacInnis, M. J., Lanting, S. C., Rupert, J. L., & Koehle, M. S. (2013). Is poor sleep quality at high altitude separate from acute mountain sickness? Factor structure and internal consistency of the Lake Louise Score Questionnaire. High Altitude Medicine and Biology, 14, 334–337. · DOI
  20. Martínez-Rodríguez, S., Iraurgi, I., Gómez-Marroquin, I., Carrasco, M., Ortiz-Marqués, N., & Stevens, A. B. (2016). Psychometric properties of the Leisure Time Satisfaction Scale in family caregivers. Psicothema, 28, 207–213. · DOI
  21. Maydeu-Olivares, A., Fairchild, A. J., & Hall, A. G. (2017). Goodness of fit in item factor analysis: Effect of the number of response alternatives. Structural Equation Modeling, 24, 495–505. · DOI
  22. McIntosh, C. N. (2007). Rethinking fit assessment in structural equation modelling: A commentary and elaboration on Barrett (2007). Personality and Individual Differences, 42, 859–867. · DOI
  23. Muthén, L. K., & Muthén, B. O. (2015). Mplus user’s guide (7th ed.). Los Angeles, CA: Muthén & Muthén.
  24. Nguyen, T. Q., Poteat, T., Bandeen-Roche, K., German, D., Nguyen, Y. H., Vu, L. K., … Knowlton, A. R. (2016). The internalized homophobia scale for Vietnamese sexual minority women: Conceptualization, factor structure, reliability, and associations with hypothesized correlates. Archives of Sexual Behavior, 45, 1329–1346. · DOI
  25. Nye, C. D., & Drasgow, F. (2011). Assessing goodness of fit: Simple rules of thumb simply do not work. Organizational Research Methods, 14, 548–570. · DOI
  26. Pearson, K. (1904). Mathematical contributions to the theory of evolution XIII: On the theory of contingency and its relation to association and normal correlation (Drapers’ Co. Research Memoirs, Biometric Series, no. 1). Cambridge, UK: Cambridge University Press.
  27. Pettersen, C., Nunes, K. L., & Cortoni, F. (2016). Does the factor structure of the aggression questionnaire hold for sexual offenders? Criminal Justice and Behavior, 43, 811–829. · DOI
  28. Sass, D. A., Schmitt, T. A., & Marsh, H. W. (2014). Evaluating model fit with ordered categorical data with a measurement invariance framework: A comparison of estimators. Structural Equation Modeling, 21, 167–180. · DOI · Paper page
  29. Satorra, A., & Bentler, P. M. (1994). Corrections to test statistics and standard errors in covariance structure analysis. In A. von Eye & C. C. Clogg (Eds.), Latent variables analysis: Applications for developmental research (pp. 399–419). Thousand Oaks, CA: Sage.
  30. Savalei, V. (2014). Understanding robust corrections in structural equation modeling. Structural Equation Modeling, 21, 149–160. · DOI
  31. Savalei, V., & Rhemtulla, M. (2013). The performance of robust test statistics with categorical data. British Journal of Mathematical and Statistical Psychology, 66, 201–223. · DOI
  32. Stander, F. W., Mostert, K., & de Beer, L. T. (2014). Organisational and individual strengths use as predictors of engagement and productivity. Journal of Psychology in Africa, 24, 403–409. · DOI
  33. Tallis, G. M. (1962). The maximum likelihood estimation of correlation from contingency tables. Biometrics, 18, 342–353. · DOI
  34. Xia, Y., Yung, Y. F., & Zhang, W. (2016). Evaluating the selection of normal-theory weight matrices in the Satorra–Bentler correction of chi-square and standard errors. Structural Equation Modeling, 23, 585–594. · DOI
  35. Yang, Y., & Xia, Y. (2015). On the number of factors in exploratory factor analysis for ordered categorical data. Behavior Research Methods, 47, 756–772. https://doi.org/10.3758/s13428-014-0499-2 · DOI
  36. Yang-Wallentin, F., Jöreskog, K. G., & Luo, H. (2010). Confirmatory factor analysis of ordinal variables with misspecified models. Structural Equation Modeling, 17, 392–423. · DOI · Paper page
  37. Yu, C.-Y. (2002). Evaluating cutoff criteria of model-fit indexes for latent variable models with binary and continuous outcomes (Doctoral dissertation). University of California, Los Angeles, CA.
  38. Yuan, K. H., & Marshall, L. L. (2004). A new measure of misfit for covariance structure models. Behaviormetrika, 31, 67–90. · DOI
  39. Zhang, W. (2008). A comparison of four estimators of a population measure of model fit in covariance structure analysis. Structural Equation Modeling, 15, 301–326. · DOI

Cited by

Metadata

Authors
Xia, Y., & Yang, Y.
Venue
Behavior Research Methods
Published
2018-06-04
Volume
51
Issue
1
Pages
409-428
ISSN
  • 1554-351X (ISSN-L)
  • 1554-3528
OpenAlex
W2806538597
Cited by
1,596
References
9
FWCI
35.18
Type
journal article
Language
en
Open access
bronze
Licence
www.springer.com/tdm (tdm)

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.